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242 results for “Spatial Dataset”
The Pacific lamprey genomic divergence, association mapping, temporal Willamette Falls, spatial rangewide datasets
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Dynamic encoding of social threat and spatial context in the hypothalamus - calcium activity recordings and behavioural dataset
<p>Social aggression and avoidance are defensive behaviors expressed by territorial animals in a manner appropriate to spatial context and experience. The ventromedial hypothalamus controls both social aggression and avoidance, suggesting that it may encode an general internal state of threat modulated by space and experience. Here we show that neurons in the mouse ventromedial hypothalamus are activated both by the presence of a social threat as well as by a chamber where social defeat previously occurred. Moreover, under conditions where the animal could move freely between a home and defeat chamber, firing activity emerged that predicted the animal's position, demonstrating the dynamic encoding of spatial context in the hypothalamus. Finally, we found that social defeat induced a functional reorganization of neural activity as optogenetic activation could elicit avoidance after, but not before social defeat. These findings reveal how the hypothalamus dynamically encodes spatial and sensory cues to drive social behaviors.</p>
Dataset for Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems
<p>Dataset for preprocessing Balmorel data in this Danish case study.</p>
U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
<p>High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future.</p> <p>The complete list of parameters is presented in the table below.</p> <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.</p> <p> </p> <p><strong>Version 1.1 updates:</strong></p> <p>1. Fill part of the data gaps in Asia. </p> <p>2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.</p>
Datasets collected for benchmarking in spatial transcriptomics
<p>Datasets collected for benchmarking in spatial transcriptomics. In addition, the code for benchmarking (March 2025 version, svg-benchmark-main.zip) is also located here and can be accessed on the GitHub website <a href="https://github.com/XiDsLab/svg-benchmark">https://github.com/XiDsLab/svg-benchmark</a>.</p>
10X Genomics Human Visium Spatial Transcriptomics Demo Dataset for Cellxgene VIP
<p>4 Visium Spatial Transcriptomics datasets downloaded 10X Genomics data site ,and organized in the way to be used for Cellxgene VIP input.</p> <p>10X_demo_data_Breast_Cancer_Block_A_Section_1<br> 10X_demo_data_Breast_Cancer_Block_A_Section_2<br> 10X_demo_data_Human_Heart<br> 10X_demo_data_Human_Lymph_Node<br> </p>
dataset for Spatial Distribution of Wildlife on University Campuses and Its Correlations with Environmental Factors: A Multi-Source Data Analysis
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Dataset belonging to "The Influence of Large-scale Spatial Warming on Jet Stream Extreme Waviness on an Aquaplanet"
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Datasets collected for Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
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Entanglement between two spatially separated atomic modes [Dataset]
<p>Entanglement between two spatially separated atomic modes [Dataset]</p>
Development and preliminary testing of a temporally controllable weather modification rocket with spatial seeding capacity: dataset
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Dataset for "Spatially resolved photoluminescence analysis of the role of Se in CdSexTe1−x thin films"
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Dataset to the manuscript titled "Mixing state, spatial distribution, sources and photochemical enhancement to sulfate formation of black carbon particles in the Arctic Ocean during summer"
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Wind Spatial Dataset
<p>This dataset comes from ten turbines in an offshore wind farm. Only the hourly wind speed data are included. The duration of the data covers two months. The longitudinal and latitudinal coordinates of each turbine are given, but those coordinates are shifted by an arbitrary constant, so that the actual locations of these turbines are protected. The relative positions of the turbines, however, remain truthful to the physical layout. The data is arranged in the following fashion. Under the header row, the next two rows are the coordinates of each turbine. The third row under the header is purposely left blank. From the fourth row onwards are the wind speed data. The first column is the time stamp. Columns 2-11 are the wind speed values measured in meters per second. This dataset is used in Chapter 3 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book.</p>
Dataset for: Spatial and environmental effects on Coho Salmon life-history trait variation
<p>Adult size, egg mass, fecundity and mass of gonads are affected by trade-offs between reproductive investment and environmental conditions shaping the evolution of life-history traits among populations for widely distributed species. Coho salmon <i>Oncorhynchus kisutch</i> have a large geographic distribution and different environmental conditions are experienced by populations throughout their range. We examined the effect of environmental variables on female size, egg size, fecundity, and reproductive investment of populations of Coho Salmon from across British Columbia using an information theoretic approach. Female size increased with latitude and decreased with migration distance from the ocean to spawning locations. Egg size decreased with average intragravel temperature during incubation, migration distance, in larger rivers, but increased in rivers that were lake headed. Fecundity increased with latitude, warmer temperature during the spawning period, and river size, but decreased in rivers that were lake headed compared to rivers with tributary sources. Gonadal somatic index increased with latitude and decreased with migration distance. Latitude of spawning grounds, migratory distance and temperatures experienced by a population, but also hydrologic features – river size and headwater source – are influential in shaping patterns of reproductive investment, particularly egg size. The lack of an effect of latitude on egg size suggest that local optima for egg size may drive the positive relationship between egg number and latitude – a pattern that is partially off-set by larger female size and gonadal somatic index with latitude.</p>
Dataset for The development of allocentric spatial frame in the auditory system
<p>Dataset for The development of allocentric spatial frame in the auditory system</p>
Covariates dataset for "Temporal harmonization of a national dataset for spatial prediction of soil organic carbon"
<p>Environmental covariates used to predict the spatial distribution of soil organic carbon for the article 'Temporal harmonization of a national dataset for spatial prediction of soil organic carbon'</p>
Spatial datasets for Victorian kelp dynamics
<p class="MsoNormal"><strong><span>Aim:</span></strong><span> Kelp forests throughout temperate regions of the world serve as foundation species that play a critical role in sustaining the health and function of marine ecosystems but are experiencing declines in abundance due to loss in resilience as the ocean clim</span><span>ate changes. Ocean warming along southeast Australia</span> has already been linked to dramatic losses of kelp species and is contributing to the range expansion and population increases of two species of sea urchin. The purpose of this research is to understand the impact of multiple stressors on the decline in kelps in this region.</p> <p class="MsoNormal"><strong>Location:</strong> Coastal waters off Victoria, Australia</p> <p class="MsoNormal"><strong>Methods:</strong> <span>In this study, we use long-term (> 20 years) datasets on biological observations across Victorian waters to determine trends in coverage and the impact of multiple environmental variables (temperature, habitat, currents, waves, connectivity, urchin abundances) on two important kelps that serve as foundation species (<em>Phyllospora comosa</em> and <em>Ecklonia radiata) </em>using boosted regression trees. These models were then used to develop predictive distribution models for each species and also to predict areas of future risk. </span></p> <p class="MsoNormal"><strong><span>Results:</span></strong><span> We found that both kelp species are decreasing in percent coverage over time and multiple environmental variables, including increasing temperatures, intensifying wave energy, changes in currents and recruitment patterns, and increases in urchin populations are all contributing to the declines of kelps. Additionally, future projections of temperature and wave energy show that these species will likely continue to decrease across 71% of Victorian waters. </span></p> <p><strong><span>Main Conclusions:</span></strong><span><strong> </strong>This information can help to better manage these important foundation species by providing maps of their current and past distributions, along with projections of climate change, to target different areas for urchin culling or macroalgae restoration to reduce future losses.</span></p>
The m-Dimensional Spatial Nyquist Limit Using the Wave Telescope for Larger Numbers of Spacecraft Dataset
<p>Dataset for: The m-Dimensional Spatial Nyquist Limit Using the Wave Telescope for Larger Numbers of Spacecraft. Includes model results of 2D and 3D modelling. </p>
Spatial dataset for ecological response models and spatial distribution of Ataeniobius toweri (Cyprinodontiformes: Goodeidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic fish Ataeniobius toweri in the Media Luna spring, Mexico. This information includes field records for the adult and juvenile stage of the species in three sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>Our original databases are those concerning the records of the species' presence by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_09.csv?versionId=9566fb68-3948-49ca-b485-2d7aac863445">Occ_records_DOMAIN_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_19.csv?versionId=6f7a0f47-4990-465c-a02c-191247c395c6">Occ_records_DOMAIN_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ad_99.csv?versionId=e9158e08-bf91-4097-828f-3ce850e1bee4">Occ_records_DOMAIN_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_09.csv?versionId=5bd51f0e-7215-4657-bfb5-bb49e6cdd57f">Occ_records_DOMAIN_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_19.csv?versionId=32d0c72a-a548-406d-8694-8d939e73058e">Occ_records_DOMAIN_At_Ju_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_DOMAIN_At_Ju_99.csv?versionId=d786131a-f445-44d2-a319-0a75aa666f3f">Occ_records_DOMAIN_At_Ju_99.csv</a>.</p> <p>From the above files, we generate the following dataset:</p> <p>The general basis of the species records by life stage and period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Occ_records_by_sector&period_At_Ad_Ju.csv?versionId=13ffb71e-ef52-43f0-9605-e416359d5997">Occ_records_by_sector&period_At_Ad_Ju.csv</a>.</p> <p>The database that includes the 500 background points generated from records of the species' presence by life stage: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_09.csv?versionId=7083cda2-a16d-4a25-9404-4c134adc2fa5">Pres_back_GLM_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_19.csv?versionId=2cbd76f6-241d-4035-b25b-1ce490362d6a">Pres_back_GLM_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ad_99.csv?versionId=c570be15-2b21-4a2e-9377-0e7962a26d1b">Pres_back_GLM_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_09.csv?versionId=11624daf-2da0-4bb1-95d4-b3c917c94c4f">Pres_back_GLM_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_19.csv?versionId=1c06bcd0-4e34-49c4-859e-100f3401e225">Pres_back_GLM_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pres_back_GLM_At_Ju_99.csv?versionId=e10d7c51-f6f8-407c-8141-1b0196d00aed">Pres_back_GLM_At_Ju_99.csv</a>.</p> <p>The databases with the extracted values of the underwater coverage (UC) and water depth (WDp) variables from the presence and background points: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_09.csv?versionId=0735fbdb-5f3d-4842-ae3e-a45e849bb12f">GBM_rel_contribution_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_19.csv?versionId=3ba6c099-e807-4e07-b1ef-32a63d347a50">GBM_rel_contribution_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ad_99.csv?versionId=49e89e06-7740-425a-be2e-db5f38c0aeca">GBM_rel_contribution_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_09.csv?versionId=fcc857f9-4332-4e15-901e-c4deecda46ab">GBM_rel_contribution_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_19.csv?versionId=34dd733a-af86-46ad-a973-4e90e9299117">GBM_rel_contribution_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/GBM_rel_contribution_At_Ju_99.csv?versionId=c7fe7fa9-e85b-45ba-9681-21f6a9cd3924">GBM_rel_contribution_At_Ju_99.csv</a>.</p> <p>The database with the results of the relative contribution for each variable by life stage and summer period: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Summary_GBM_At_Ad_Ju.csv?versionId=f0b0b4d4-62b0-4d95-b6f4-ec5e00b97633">Summary_GBM_At_Ad_Ju.csv</a>.</p> <p>The databases containing the correlation values between the UC and WDp variables, by Pearson's method, for each point of presence and background: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_09.csv?versionId=99e74579-6edf-46e2-8f5d-3dd06a9a325f">Pearson_corr_At_Ad_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_19.csv?versionId=ea4d938e-133e-426d-b11a-85ed877876f8">Pearson_corr_At_Ad_19.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ad_99.csv?versionId=a499ab48-6be0-447c-9521-8e500769d4de">Pearson_corr_At_Ad_99.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_09.csv?versionId=da583fda-0bc5-4769-bab8-bfb3c365232e">Pearson_corr_At_Ju_09.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_19.csv?versionId=88e89cc1-2bfa-45cb-b6af-c87737a6fbd7">Pearson_corr_At_Ju_19.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/Pearson_corr_At_Ju_99.csv?versionId=fddca19c-fa6b-460d-a571-966229fad955">Pearson_corr_At_Ju_99.csv</a>.</p> <p>And, the databases that contain the probability values for the generation of the ecological response curves in function of the UC variable: <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_09_Prob_UC.csv?versionId=240d8a0c-c0f6-4a1f-8983-74320b0863f8">At_Ad_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_19_Prob_UC.csv?versionId=2bff87d1-b910-4cfd-b521-881e56034b19">At_Ad_19_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ad_99_Prob_UC.csv?versionId=02050376-e34d-46ac-bdf9-7bc7b1f8570f">At_Ad_99_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_09_Prob_UC.csv?versionId=a3182956-bbf4-4853-a3a2-4296e7acf79c">At_Ju_09_Prob_UC.csv</a>, <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_19_Prob_UC.csv?versionId=21a89330-89d3-442a-a801-19842a3bed70">At_Ju_19_Prob_UC.csv</a> y <a href="https://zenodo.org/api/files/05c9851d-72bc-406a-baee-4b09a28882b9/At_Ju_99_Prob_UC.csv?versionId=ff7d3966-6248-49d2-9000-07e374f0ba76">At_Ju_99_Prob_UC.csv</a>.</p> <p>For more information about the codes where the previous dataset was generated, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Also, to download the UC and WDp variables to run the modeling processes, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.